Automatic image segmentation methods and analysis

ABSTRACT

The invention provides methods and apparatus for image processing that perform image segmentation on data sets in two- and/or three-dimensions so as to resolve structures that have the same or similar grey values (and that would otherwise render with the same or similar intensity values) and that, thereby, facilitate visualization and processing of those data sets.

PRIORITY CLAIM

This application is a continuation of (1) U.S. application Ser. No.15/988,519 filed May 24, 2018, which claims priority to (2) U.S.application Ser. No. 15/276,546 filed Sep. 26, 2016, which issued May29, 2018 as U.S. Pat. No. 9,984,460 and claims priority to (3) U.S.application Ser. No. 14/040,215 filed Sep. 27, 2013, which issued Sep.27, 2016 as U.S. Pat. No. 9,454,813 and claims priority to (4) U.S.application Ser. No. 12/275,862 filed Nov. 21, 2008 which issued Oct. 1,2013 as U.S. Pat. No. 8,548,215 and claims the benefit of priority of(5) U.S. Provisional Patent Application Ser. No. 60/989,915, filed Nov.23, 2007; the teachings of (1)-(5) are explicitly incorporated herein byreference in their entireties and for all purposes.

BACKGROUND OF THE INVENTION

The invention pertains to digital data processing and, moreparticularly, to the visualization of image data. It has application, byway of non-limiting example, in medical imaging, microscopy, geophysics,non-destructive testing.

Data sets in diagnostic medical imaging and other disciplines such asmicroscopy, geo-physics, non destructive testing etc., are growing insize and number. Efficient visualization methods are thereforeincreasingly important to enable clinicians, researchers and others toanalyze digital image data. Image segmentation and the extraction ofstructures from images for visualization and analysis can be helpful forthis purpose.

Image segmentation is an automated technique that facilitatesdistinguishing objects and other features in digital images. Thetechnique can be used, for example, to simplify digitized images so thatthey can be more readily interpreted by computers (e.g., image analysissoftware) and/or by their users. Thus, for example, image segmentationcan be used to simplify a digitized x-ray image of a patient who hasconsumed a barium “milkshake.” In its original form, such an image ismade up of pixels containing a wide range of undifferentiated intensityvalues that—although, possibly recognizable to the human eye as skeletalbones and digestive tract—are largely uninterpretable by a computer.

Image segmentation can remedy this by categorizing as being of potentialinterest (e.g., “not background”) all pixels of a selected intensityrange—typically, all intensities above a threshold value. Alternatively,image segmentation can rely on finding all edges or borders in theimage. A related, but still further alternative technique, is toidentify all “connected components”—i.e., groupings of adjacent pixelsin the image of the same, or similar, pixel intensity (or color). Yetanother image segmentation technique involves “region growing,” in whichconnected components are grown around seed point pixels known to residewithin structures of interest.

Continuing the example, threshold-based segmentation can be applied toan x-ray image such that pixels whose intensities are above, say, 200(out of 255) are labelled as barium-containing digestive organs and allother pixels are labelled as background. If the pixel intensities of theformer are uniformly adjusted to a common value of, say, 255, and thepixel intensities of the latter are uniformly adjusted to, say, 0, theresulting “segmented” image, with only two levels of intensity values (0and 255) is often more readily interpreted by man and machine alike.

An object of the invention is to provide improved methods and apparatusfor digital data processing.

A related object is to provide such improved methods and apparatus forthe visualization of image data.

A still further related aspect of the invention is to provide suchmethods and apparatus as can be applied in medical imaging, microscopy,geophysics, non destructive testing, and other imaging applications.

SUMMARY OF THE INVENTION

The invention provides methods and apparatus for image processing thatperform image segmentation on data sets in two- and/or three-dimensionsso as to resolve structures that have the same or similar grey values(and that would otherwise render with the same or similar intensityvalues) and that, thereby, facilitate visualization and processing ofthose data sets.

Such methods and apparatus can be used, for example, to apply differentvisualization parameters or rendering methods to different structures orregions of the data set, including completely hiding one or more ofthose regions. In particular, for example, aspects of the inventionprovide methods and apparatus for medical imaging that automaticallyextract bone structures in computed tomography (CT) 3D run-off studieswithout removing vessel structures (although those vessel structures mayhave the same or similar grey values). Further aspects of the inventionprovide for automatic removal of these and/or other structures shown in3D (and other dimensional) images generated by other medical imagingtechniques.

Thus, in one aspect, the invention provides a method for processing oneor more two-dimensional (2D) image slices of a three-dimensional (3D)volume. The method includes the steps of identifying a region of the 3Dvolume to which the 2D image belongs, and performing segmentation on the2D image to identify connected components. The method further calls forlabeling pixels of those connected components based on geometriccharacteristics (e.g., shape and/or size), where such labeling is basedon a volumetric region to which the 2D image belongs. By way of example,methods of the invention can be applied to processing 2D image “slices”generated via computed tomography, e.g., in medical imaging. For thesepurposes, the “region” is a region of the patient's body.

Related aspects of the invention provide such methods wherein the stepof identifying the region to which the 2D image belongs includescomputing a histogram of the image and comparing it with one or moreother histograms, e.g., from an atlas of average histograms previouslydetermined for the 3D volume (such as, for example, an atlas of averageCT histograms for the human body). The image can be assigned, forexample, to the region of the 3D volume associated with thebest-matching histogram from the atlas. In instances where multiple 2Dimages are assigned to regions, the method can include checking thoseassignments for consistency and re-assigning one or more 2D images toanother region, as necessary.

Still further related aspects of the invention provide such methodswherein the step of identifying the region to which the 2D image belongsincludes determining additional information about the 2D image byperforming threshold segmentation and determining therefrom any of (a) anumber of connected components in the resulting image and/or (b) a totalarea occupied by selected structures and/or types of structures (e.g.,body tissues). Alternatively, or in addition, the step of determiningadditional information can include computing a histogram of selectedstructures (e.g., a histogram of a region within the body and/or aregion covered by body tissues).

Other aspects of the invention provide methods as described above inwhich the step of performing segmentation on the 2D image includesperforming a threshold segmentation to label identically pixelsrepresenting the structures of interest. When such methods are appliedto processing CT images, for example, such structures may be vasculature(e.g., blood vessels) and bone.

Such methods can, according to related aspects of the invention, includeidentifying connected components in the 2D image, following thresholdsegmentation, and relabeling pixels as differing types of structures ofinterest (e.g., vasculature versus bone, and vice versa) based ongeometric properties of those connected components, e.g., in the contextof the (body) region to which the 2D image has been assigned.

Still further related aspects of the invention provide such methods inwhich the step of performing segmentation on the 2D image to identifyand label connected components based on their geometric characteristicsincludes performing such segmentation with increasingly largerthresholds to identify connected components that separate, as a result,into multiple components. When this is detected, the method calls forrelabeling as differing types of structures of interest (again, by wayof example, vasculature versus bone, and vice versa) making up thoseseparated components.

Still further aspects of the invention provide methods as describedabove that include performing segmentation on a 3D data set formed fromthe plurality of the 2D images so as to label voxels representing thestructures of interest (e.g., vasculature versus bone). According tothese aspects of the invention, such segmentation can includeflood-filling the 3D data set from one or more seed points placed atvoxels in such structures of interest.

Flood-filling can proceed with increasing voxel intensity ranges until aconnected component or region created thereby overextends. In relatedaspects of the invention, flood-filling is terminated when a connectedcomponent formed thereby either (i) stair-steps, or (ii) overruns a seedpoint placed in a voxel representing another structure of interest.

According to still further related aspects of the invention, the methodcalls for using model-based detection to find structures of interest inthe 3D data set. And, yet, in still further related aspects of theinvention, pixels corresponding to voxels labeled using such 3Dsegmentation are not relabeled if and when 2D segmentation of the typedescribed above is performed.

These and other aspects of the invention are evident in the drawings andthe description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the invention may be attained byreference to the drawings, in which:

FIG. 1A depicts a digital data processing environment of the type inwhich the invention is practiced;

FIG. 1B overviews a method according to the invention;

FIG. 2 depicts a result of segmentation of a data set of imagesutilizing two-dimensional and/or three-dimensional segmentation-basedmethods according to the invention;

FIG. 3 depicts a method according to the invention for assigning a 2Dimage slice to a volumetric region;

FIG. 4 depicts a method according to the invention for 2D segmentation;

FIG. 5 depicts a method according to the invention for 3D segmentation;

FIGS. 6-8 depict a melding and segregation of structures in an imagesubject to 2D segmentation according to the invention.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENT Overview

Described below are methods and apparatus for image processing accordingto the invention. These may be realized on workstations, personalcomputers, and other digital data processing systems of the type knownin the art and used for image processing and/or reconstruction, anexample of which is provided in FIG. 1A.

The system 10 includes a central processing unit (CPU) 30, dynamicmemory (RAM) 32, and I/O section 34, all of the type known in the art,as adapted in accord with the teachings hereof. The digital dataprocessor 26 may be coupled, via I/O section 34, with an image source(not shown), as well as with a monitor or other presentation device 28on which images processed by system 10 may be displayed. I/O section 34can also provide coupling to other apparatus (e.g., storage devices)and/or networks to which processed images can be transmitted forstorage, displayed or otherwise. Illustrated digital data processor 26may also include one or more graphical processing units (GPUs) 36, orother hardware acceleration devices, to facilitate execution of themethods described herein, though, such is not a requirement of theinvention. Moreover, although digital data processor 26 is shown herehas a stand-alone device, in other embodiments, it may be arrangedotherwise, e.g., as an embedded processor.

The image source generates or otherwise supplies a plurality of images14 for segmentation. In the illustrated embodiment, these represent,individually, two-dimensional (2D) “slices” of a three-dimensional (3D)volume. Indeed, in the example discussed below, they represent axialslices of a computed tomography (CT) scan of a patient's body, orportion thereof. More generally, however, images supplied by source 12may derive from any number of technical pursuits, medical andnon-medical alike, such as, by way of non-limiting example, microscopy,geophysics, non destructive testing, and so forth. Furthermore, althoughreferred to as such in the illustrated embodiment, in other embodiments“images” 14 may comprise two-dimensional slices of data of any naturethat make up the 3D volume.

FIG. 1B overviews operation of the system 10 and, more particularly, ofcomputer software 40 executing thereon for purposes of segmenting images14. Although such methods according to the invention are implemented incomputer software (or, a “computer program”) in other embodiments, suchapparatus and/or methods may be implemented solely in hardware, infirmware or otherwise.

Referring to the drawing, the system 10 and, more particularly, thesoftware 40 executes the following operations: [0035] First the imagedata is loaded into main memory 32 of the computer 26. See, step 42.This can be effected all at once or in stages, e.g., as image data fromthe respective slices is needed for processing. In the illustratedembodiment, that image data comprises the plurality of 2D images 14 thatmake up a 3D volume (hence, the image data is referred to as a “3D dataset”). In other embodiments, processing is performed on only a singleindividual 2D image.

Then, specific structures represented in the images are segmented, i.e.,pixels (and voxels) belonging to those structures are marked (or“labeled”) in a suitable data structure in memory 32. See, step 44. Ininstances where the images 14 are from medical imaging apparatus, thestructures can be, for example, anatomical structures. The datastructure may comprise any collection of data elements (e.g., an array,struct, linked list, etc.) of the type known in the art (as adopted foruse herewith) suitable for holding labels that designate structures towhich the pixels and/or voxels belong.

Then the processed image data is displayed, e.g., on output device 28,using different rendering settings for the different structures. Seestep 46. In other embodiments, the processed image data may be subjectedto still further processing on device 26, before, after, or in lieu ofdisplay. Alternatively, or in addition, it may be transmitted elsewhere(e.g., to other equipment and/or locations) for storage, processing,and/or display.

FIGS. 3-5 depict further details of the processing effected in step 44.The discussion of those figures below uses, as an example, segmentationof bone and blood vessels in the images 14, e.g., to enable the formerto be removed during display of the segmented images in step 46.However, as noted above, it will be appreciated that the invention canbe used for a variety of other purposes and in a variety of otherapplications.

To understand the example, it will be appreciated that, often, CTstudies use contrast agent to visualize the blood vessels. In such casesthe blood vessels appear bright in the CT images, similar to bonestructures. When rendering the image in maximum intensity projectionmode (MIP), then the bright bone structures will often occlude thevessel structures which are typically much thinner than bone. Howeversince both contrasted vessels and bone have the same or similarintensity values, standard rendering methods to distinguish them, suchas assigning different transparency values based on intensity, willfail. Apparatus and methods according to the invention resolve this,allowing for the segmentation of structures represented in images14—i.e., structures that would otherwise render with the same or similarintensity values—so that they may be rendered (and/or processed)differently from one another.

In the example that follows, those methods are applied to segment pixelsrepresenting bone differently from those representing blood vessels sothat they can be rendered differently and, particularly, so that thebone can be rendered invisibly in step 46. FIG. 2 illustrates the resultof executing such a method. In the left hand half of the figure amaximum intensity projection (MIP) of a CT runoff study is shown. Mostof the vascular structure is hidden by the bone. In the right hand halfof the figure voxels belonging to bone were segmented using methods ofthe invention and then left out in the rendering, thus, revealing thevessel structure. Note that in the discussion that follows, the images14 are referred to, individually, as “slices,” “2D slices,” or the likeand, collectively, as the “data set,” “3D data set,” or the like.

Identifying Body Regions

Referring to FIG. 3, the illustrated method first identifies volumetricregions to which the respective image slices 14 belong. In the example,these are body regions, i.e., ranges of successive slices correspondingto certain anatomy, such as foot, lower leg, knee, upper leg, hip, lowerabdomen, lung. This is done as follows:

A histogram is computed for each slice of the data set. See step 48. Thehistogram is computed in the conventional manner known in the art.

The histograms are then used to determine which volumetric regions therespective slices belong to. In the example, the histograms are comparedwith an anatomic atlas containing average histograms for the differentbody regions. See step 50. For each of the slices a correlation such asa cross-correlation is computed between the slice histogram and saidatlas of average histograms in order to determine which body part'shistogram the slice most closely matches and therefore most likelybelongs to. See step 51. The slice is then assigned to the respectivebody part. See step 52.

Once all (or a subset of all) of the slices have been assigned, then itis checked whether the individual slice assignment is consistent withthe overall volumetric region to which they pertain. In the example, theslice assignments are checked to insure that they are consistent withhuman anatomy, e.g. in the above example, there can only be one range ofslices assigned to each of the body regions such as “lower leg” and thebody regions must be ordered in the correct spatial order. See step 54.

If inconsistencies are detected, slices are re-assigned to differentvolumetric regions—in the example, body regions—to obtain a consistentoverall assignment. In many instances, multiple different sets of slicescould be chosen for re-assignment in order to achieve overallconsistency. Preferably, methods and apparatus according to theinvention choose the one which re-assigns the smallest number of slicespossible to achieve consistency.

As those skilled in the art will appreciate, additional criteria (orparameters) can be utilized to match slices to atlas entries—or, moregenerally, to determine which volumetric regions the respective slicesbelong to. This is illustrated in steps 56-64. Generally speaking, thisinvolves performing a threshold segmentation on each slice and, then,determining (a) the number of connected components in the image and/or(b) the total area occupied by pixels representing structures and/ortypes of structures of interest (in the example, body tissues). It mayalso involve computing a second histogram for the latter, i.e.,structures of interest (e.g., pixels within the body).

More particularly, by way of example, in the illustrated embodimentadditional parameters for processing axial slices of a CT scan, e.g.,for segmentation of bone and blood vessels, can be computed in a mannersuch as the following:

A threshold segmentation is performed in each slice with a threshold of−1000 HU (Hounsfield Units) which separates body tissue from air. Seestep 58. The number of connected components in the threshold segmentedimage is computed, as well as the total area covered by body pixels. Seesteps 60-62. A second histogram (subsequently referred to as BodyHistogram) is computed for each slice which only takes into accountpixels inside the body, i.e., enclosed in a connected component in saidthreshold segmented image. See step 64. It will be appreciated that, insome applications, the threshold used to separate body tissue from airmay vary (e.g., from about −1000 HU to other values) and that, in otherembodiments, still other thresholds are used, e.g., to distinguish amongother structures and/or types of structures of interest.

The additional information determined for each slice following thresholdsegmentation (in step 58)—namely, the number of connected components,the total area occupied by structures of interest (e.g., body tissue),and/or a histogram of those structures—can be used along with (orinstead of) the slice histogram computed in step 48, to determinevolumetric regions (e.g., body regions) to which the respective imageslices 14 belong.

In the example, the additional information is used to assign slices tobody regions and/or anatomical structures using criteria such as, forexample, “slices which have a significant number (>25%) of air pixels(pixels with intensity <−1000 HU) are assigned to lung” or “the numberof connected components in slices in region lower leg is two” or “theknee region is between 2 cm and 20 cm in size”. Adding such criteria canmake the illustrated method and apparatus more robust and/or moreefficient. The exact selection of criteria is application- and image-setdependent: for example a criterion like “the number of connectedcomponents in slices in region lower leg is two” should not be chosen ifone-legged patient data are to be processed.

Performing Segmentation in Slices

Once the slices have been assigned to volumetric regions (in theexample, body regions), 2D segmentation is performed to identifyconnected components of potential interest (bone and vessel) and theirpixels labelled (or categorized) based on geometric characteristics. Inthe example, this differentiates pixels depicting bone from bloodvessels, though, in other applications it may differentiate pixelsdepicting other structures from one another. In that light, it will beappreciated that some of the algorithms discussed below are specific tobone/vessel segmentation and that different algorithms and/or thresholdsmay be employed in different applications. Moreover, even in the contextof bone/vessel segmentation, the algorithms may vary within differentregions of the body as will be described in the following.

In each slice, a threshold segmentation is performed using a firstthreshold. See step 66. A value is chosen so as to result in identicallabeling of pixels representing the structures of interest (in theexample, bones and blood vessels), yet, different labeling forstructures that are not of interest (e.g., organs). In the example, thisis a threshold of 130 HU—though it will be appreciated that differentthreshold values (e.g., those of about 130 HU or otherwise) can be useddepending on the acquisition protocol and user input. As noted, thissegmentation will label both, bone and vessels.

In the next step, connected components in each slice are (re-)labelledbased on their geometries characteristics (e.g., size and/or shape).This labeling is preferably done in the context of the volumetric regionto which the respective slice is assigned. See step 68. Bones normallyhave a greater diameter than vessels. Therefore, in the example, aftersaid threshold segmentation all connected components with an areagreater than the diameter of the largest vessel which can occur in therespective body region is assigned to bone, all others are assigned tovessel. As noted, this may be further qualified by the context of thebody region to which the slice is assigned. Thus, in the leg, except forthe knee region, one can add the additional constraint that only thelargest connected component (upper leg) or the two largest connectedcomponents (lower leg) are to be considered bone. Caution is requiredwhen cases are to be processed in certain applications were pathologiesare present which could violate those latter criteria, such as patientswith bone fractures.

Sometimes the structures that will ultimately be differentiated (in theexample, bones and blood vessels) run very close to one another. In suchcases, in some slices the cross section of one structure (e.g., thebone) and of another structure (e.g., the vessel) near it may actuallyappear melted into one connected component in the threshold segmentationgenerated in step 66. This is illustrated in FIG. 6, which shows part ofan axial slice in the lower leg. The two bones and some vessels can beclearly seen as bright spots. FIG. 7 shows a threshold segmentation witha certain threshold (268 HU). As can be seen, one vessel which runsclose to the upper bone appears to be connected in this segmentation.Therefore if the whole connected component was selected as bone, thenthat piece of the vessel would be segmented incorrectly.

The method used to prevent that is illustrated in steps 70-74 andinvolves performing segmentation with increasingly larger thresholds,checking after each successive segmentation to see if another otherwiseseemingly connected component separates into multiple components and, ifso, relabeling the separated components as different structures.Application of this to the example follows:

Assume that a connected component is identified as bone in one slice. Wewill refer to (and label) the set of pixels comprising this component asB. Now the threshold is increased from the first or originalthreshold—in this case, 130 HU—in steps of 1 HU up to 400 HU. See step70. Of course, it will be appreciated that other step sizes may be used.When increasing the threshold, the segmented bone structure shrinks.Often, in the case of a melted vessel/bone structure, the vessel willseparate in the course of this process. This can be seen in FIGS. 7 and8. While in FIG. 7 the vessel and the upper bone are still connected, inFIG. 8 with a higher threshold, they are separated.

After each increase of the threshold it is checked whether a connectedcomponent previously identified as bone gets separated into multiplecomponents, a large one and one and a small one. See step 72. Once thathappens, the smaller component is labelled as vessel. See step 74. Welabel the set of pixels comprising said smaller component as V and theset of pixels comprising said larger component as B*.

In the original segmentation with the first threshold (here, 130 HU),all pixels belonging to B are now reassigned according to the followingcriterion: Any pixel which is part of B* remains bone. Any pixel whichis part of V is assigned to vessel, i.e. removed from B. Any pixel whichis neither part of B* nor V is re-assigned to vessel if it is closer toV than to B*. The distance of a pixel p to a set of pixels S is definedto be the shortest distance between p and any of the pixels belonging toS.

Performing Segmentation in 3D

In addition to (or in lieu of) performing segmentation on the 2D imageslices 14 as described above, methods and apparatus according to theinvention performs segmentation on 3D data sets formed from those slicesutilizing flood-filling (or region growing). This permits labelingvoxels representing structures of interest (e.g., those representingbone and blood vessels) and, as a consequence, provides further basisfor differentially labeling the corresponding voxels (and pixels).

By way of overview, this 3D segmentation involves placing seed points invoxels which are known to represent structures of interest andflood-filling from those seed points to mark connected componentsbelonging to the same respective structures. The voxel intensity rangefor the flood-fill is gradually increased until the connectedcomponents—in this case, regions or volumes—formed thereby stair-step oroverrun seed points for other structures.

An advantage of 3D segmentation (used alone or in connection with 2Dsegmentation) is that it can take advantage of structures that are morereadily identifiable in 3D (i.e., across multiple slices) than inindividual 2D slices. For example, in the abdominal and lung region,blood vessels like the aorta may have diameters larger than some of thethinner bone structures. While the procedures discussed in the precedingsection might therefore result in the aorta being marked as vasculature,and vice versa, use of 3D segmentation in accord herewith circumventsthe problem.

Therefore in the illustrated embodiment the above described 2D method isextended as follows using a three dimensional approach: Seed points areplaced in voxels which are known to be either bone or vessel. Then fromthese seed points a flood fill is started to mark connected voxelsbelonging to the same structure. Measures are taken to avoid the floodfill from “spilling over” from vessel into bone structures or viceversa.

In the example this is implemented as follows: Anatomic structures inthe pelvic and abdomen region are searched which can be easily foundwith model based detection methods. See, step 76. For example the aortais a tubular structure with a known diameter range, always rathercentered and always located in front of the spine structure. Also thepelvis bone can be located by searching in the pelvic region for thelargest 3D-connected component.

Once those components are identified, seed points can be placed intothese objects. See, step 78.

Then from these seed-points a flood-fill algorithm is started. See, step80. The flood-fill first uses a very conservative intensity rangemeaning that only voxels with an intensity very similar to the seedpoints are marked. See step 82. Then, the range is grown to fill/markmore voxels belonging to the same anatomic structure. See, step 84. Ifthe range was made too wide, the flood-fill could erroneously spread outinto a spatially close structure. This is the reverse process of theabove described melting/separation problem for vessels running nearbones. For example with a seed point placed in the aorta, the flood fillcould spill over into the spine if the range was chosen such that thosetwo structures would meld. This must be avoided. To do so, step 86 isexecuted and a number of criteria are evaluated while growing theintensity range.

(i) If the volume filled by the flood fill stair steps, whencontinuously increasing the range, then it must be assumed that theflood-fill has spilled over and the range is reduced again. See, steps88 and 90.

(ii) If the flood fill reaches a voxel which is marked as a seed pointfor a different structure then also it must be assumed that the range istoo wide and the range is reduced again. See, steps 90 and 92. Forexample if a flood fill is started at a seed point in the aorta and itreaches a point marked bone in the pelvis, then the range was obviouslychosen such that the vessel tree and the bones are connected relative tothis range. Therefore this range must be rejected and narrowed.

Any voxels which are marked by the above method can be marked bone orvessel respectively, depending on which seed point they were connectedto (or grown from). The pixels correspond to such voxels do not need tobe re-labeled when this method is combined with the slice basedsegmentation described above.

Following segmentation of a data set of images 14 utilizingtwo-dimensional and/or three-dimensional segmentation in the mannerdiscussed above, the data set (and/or individual images 14 containedtherein) can be displayed on output device 28. Different renderingsettings can be set for the structures labelled by such segmentation,e.g., to facilitate visualization. For example, as shown on the rightside of FIG. 2 and discussed above, voxels labelled as bone can berendered invisibly, while voxels labelled as blood vessels can berendered in MIP mode. Of course, other display settings can be usedinstead, e.g., with blood vessels rendered in one color and bone inanother color, and so forth. Moreover, as noted above, the data set maybe subjected to still further processing on device 26, before, after, orin lieu of display. Alternatively, or in addition, it may transmittedelsewhere (e.g., to other equipment and/or locations) for storage,processing, and/or display.

The embodiments described herein meet the objects set forth above, amongothers. Thus, those skilled in the art will appreciate that theillustrated embodiment merely comprises an example of the invention andthat other embodiments making changes thereto fall within the scope ofthe invention, of which we claim:

1. A method of revealing vasculature in a CT runoff study comprising:(a) receiving a plurality of 2D image slices in a CT runoff studycorresponding to an upper leg; (b) selecting a threshold to label one ormore pixels as corresponding with a bone structure in the plurality of2D image slices using a digital data processing system configured forimage processing; (c) carrying out a threshold segmentation on aplurality of pixels in the plurality of 2D image slices using thethreshold selected in step (b); (d) selecting a 2D image slice from theplurality of 2D image slices corresponding to the upper leg; (e)identifying one or more bone structure pixels in the upper leg in the 2Dimage slice based on the threshold segmentation; (f) determining an areaoccupied by the bone structure in the 2D image slice; (g) if the area isa largest connected component in the 2D image slice then identifying oneor more additional bone structure pixels in the area: (h) flood fillingwith the digital data processing system the one or more bone structurepixels and the one or more additional bone structure pixels to identifythe bone structure; (i) extracting the bone structure from the 2D imageslice; and (j) removing the bone structure from the 2D image slice inorder to reveal vasculature using the digital data processing systemconfigured for image processing.
 2. The method of claim 1, where a pixelis not assigned to the bone structure when the pixel would generate astair-step effect.
 3. The method of claim 1, where a pixel is notassigned to the bone structure when the pixel is assigned tovasculature.
 4. The method of claim 1, where a pixel of the plurality ofpixels is not assigned to the bone structure based on an intensity ofthe pixel below the threshold.
 5. The method of claim 1, where a pixelof the plurality of pixels is assigned to the bone structure based on anintensity of the pixel above the threshold.
 6. The method of claim 1,where the threshold is between: a lower limit of 130 Hounsfield Units(HU); and an upper limit of 400 HU.
 7. The method of claim 6, wheresteps (b) through (k) are repeated with an incrementally increasedthreshold.
 8. The method of claim 7, where the threshold is increased by1 HU.
 9. The method of claim 1, further comprising labeling as one ormore additional bone structure pixels one or more pixels adjacent one ormore bone structure pixels.
 10. The method of claim 1, furthercomprising labeling as one or more additional bone structure pixelsbased on connectedness of one or more bone structure pixels.
 11. Themethod of claim 1, further comprising labeling as one or more additionalbone structure pixels one or more pixels based on one or more geometriccharacteristics selected from the group consisting of shape of the bonestructure in the 2D image slice and size of the bone structure in the 2Dimage slice.
 12. The method of claim 1, further comprising determining adiameter occupied by the bone structure in the 2D image slice, andlabeling as one or more additional bone structure pixels one or morepixels bounded by the diameter.
 13. The method of claim 1, where thethreshold is between: a lower limit of 130 Hounsfield Units (HU); and anupper limit of 1000 HU.
 14. The method of claim 1, where at least afirst pixel of the plurality of pixels with an intensity below thethreshold is set to an intensity of 0, where at least a second pixel ofthe plurality of pixels with an intensity above the threshold is set toan intensity of
 255. 15. A method of revealing vasculature in a CTrunoff study comprising: (a) receiving a plurality of 2D image slices ina CT runoff study corresponding to a lower leg; (b) selecting athreshold to label one or more pixels as corresponding with a bonestructure in the plurality of 2D image slices using a digital dataprocessing system configured for image processing; (c) carrying out athreshold segmentation using the threshold selected in step (b) on aplurality of pixels in the plurality of 2D image slices; (d) selecting a2D image slice from the plurality of 2D image slices corresponding tothe lower leg; (e) identifying one or more bone structure pixels in thelower leg in the 2D image slice based on the threshold segmentation; (f)determining an area occupied by the bone structure in the 2D imageslice; (g) if the area is one of two largest connected components in the2D image slice then identifying one or more additional bone structurepixels in the area: (h) flood filling with the digital data processingsystem the one or more bone structure pixels and the one or moreadditional bone structure pixels to identify the bone structure; (i)extracting the bone structure from the 2D image slice; and (j) removingthe bone structure from the 2D image slice in order to revealvasculature using the digital data processing system configured forimage processing.
 16. The method of claim 15, further comprisinglabeling as one or more additional bone structure pixels one or morepixels neighboring one or more bone structure pixels.
 17. The method ofclaim 15, further comprising labeling as one or more additional bonestructure pixels based on connectedness of one or more bone structurepixels.
 18. The method of claim 15, further comprising labeling as oneor more additional bone structure pixels one or more pixels based on oneor more geometric characteristics selected from the group consisting ofshape of the bone structure in the 2D image slice and size of the bonestructure in the 2D image slice.
 19. The method of claim 15, furthercomprising determining a diameter occupied by a first bone structure inthe 2D image slice, and labeling as one or more additional bonestructure pixels one or more pixels bounded by the diameter.
 20. Amethod of revealing vasculature in a CT runoff study comprising: (a)receiving a plurality of 2D image slices in a CT runoff study measuredwith a contrast agent corresponding to an anatomical region, where theanatomical region is selected from the group consisting of foot, lowerleg, knee, upper leg, hip, lower abdomen and lung; (b) adjusting athreshold to label one or more pixels as corresponding with a first bonestructure in the plurality of 2D image slices using a digital dataprocessing system configured for image processing; (c) carrying out athreshold segmentation on a plurality of pixels in the plurality of 2Dimage slices, where at least a first pixel of the plurality of pixelswith an intensity below the threshold is set to an intensity of 0, whereat least a second pixel of the plurality of pixels with an intensityabove the threshold is set to an intensity of 255; (d) selecting a 2Dimage slice from the plurality of 2D image slices corresponding to theanatomical region; (e) identifying one or more first bone structurepixels in the anatomical region in the 2D image slice based on thethreshold segmentation; (f) determining an area occupied by the firstbone structure in the 2D image slice; (g) determining a diameteroccupied by a largest vessel in the 2D image slice; (h) if the area isgreater than the diameter then identifying one or more additional firstbone structure pixels in the area: (i) flood filling with the digitaldata processing system the one or more first bone structure pixels andthe one or more additional first bone structure pixels to identify thefirst bone structure; (j) extracting the first bone structure from the2D image slice; and (k) removing the first bone structure from the 2Dimage slice in order to reveal vasculature using the digital dataprocessing system configured for image processing.